'Getting ValueError: Data Params Error when using masterful.trainer.train

I followed the tutorial here to try to train my model using CIFAR-100. But I'm getting this error. What do I do?

ValueError: Data Params Error: The dataset label shape (100,) does not match the
                number of classes (10) in the dataset. Please ensure the dataset
                labels have 10 classes, or change the number of classes
                to match the dataset.

Here is my setup, adapted from the tutorial but for CIFAR-100.

import tensorflow as tf
import tensorflow_datasets as tfds

import masterful

masterful = masterful.register()

TRAINING_PERCENTAGE = 5
(training_dataset,
 test_dataset) = tfds.load('cifar100',
                           as_supervised=True,
                           split=[f'train[:{TRAINING_PERCENTAGE}%]', 'test'],
                           with_info=False)


def sparse_to_dense(image, label):
  label = tf.cast(label, tf.int32)
  one_hot_label = tf.one_hot(label, depth=100)
  return image, one_hot_label


training_dataset = training_dataset.map(sparse_to_dense,
                                        num_parallel_calls=tf.data.AUTOTUNE)
test_dataset = test_dataset.map(sparse_to_dense,
                                num_parallel_calls=tf.data.AUTOTUNE)


def get_model():
  model = tf.keras.models.Sequential()
  model.add(
      tf.keras.layers.experimental.preprocessing.Rescaling(1. / 255,
                                                           input_shape=(32, 32,
                                                                        3)))
  model.add(tf.keras.layers.Conv2D(
      16,
      (3, 3),
      activation='relu',
  ))
  model.add(tf.keras.layers.GlobalAveragePooling2D())
  model.add(tf.keras.layers.Dense(10))
  return model


model = get_model()

model_params = masterful.architecture.learn_architecture_params(
    model=model,
    task=masterful.enums.Task.CLASSIFICATION,
    input_range=masterful.enums.ImageRange.CIFAR10_TORCH,
    num_classes=10,
    prediction_logits=True,
)
training_dataset_params = masterful.data.learn_data_params(
    dataset=training_dataset,
    task=masterful.enums.Task.CLASSIFICATION,
    image_range=masterful.enums.ImageRange.CIFAR10_TORCH,
    num_classes=10,
    sparse_labels=False,
)

optimization_params = masterful.optimization.learn_optimization_params(
    model,
    model_params,
    training_dataset,
    training_dataset_params,
)

# This is a set of parameters learned on CIFAR10 for
# small sized models.
regularization_params = masterful.regularization.parameters.CIFAR10_SMALL

training_report = masterful.training.train(
    model,
    model_params,
    optimization_params,
    regularization_params,
    None,
    training_dataset,
    training_dataset_params,
)

tensorflow keras

Solution 1:^[1]

Both of you are correct, regularization and classification is support 10 classes you may consider multiple steps approaches.

[ Sample ]:

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
TRAINING_PERCENTAGE = 5
(training_dataset, test_dataset) = tfds.load('cifar100', as_supervised=True, split=[f'train[:{TRAINING_PERCENTAGE}%]', 'test'], with_info=False)

training_dataset = training_dataset.map(sparse_to_dense, num_parallel_calls=tf.data.AUTOTUNE)
test_dataset = test_dataset.map(sparse_to_dense, num_parallel_calls=tf.data.AUTOTUNE)
task = masterful.enums.Task.CLASSIFICATION

[ ENUM ]:

# Task.CLASSIFICATION
# Task.CLASSIFICATION
# Task.BINARY_CLASSIFICATION
# Task.MULTILABEL_CLASSIFICATION
# Task.DETECTION
# Task.LOCALIZATION
# Task.SEMANTIC_SEGMENTATION
# Task.INSTANCE_SEGMENTATION
# Task.KEYPOINT_DETECTION

[ Statistics ]:

# @enum.unique
# class DatasetStatistics(enum.Enum):
  # """An enum for datasets statistics required for Masterful's normalization."""
  # CIFAR_10_MEAN = [0.4914, 0.4822, 0.4465]
  # CIFAR_10_STD = [0.247, 0.243, 0.261]
  # CIFAR_10_BGR_255 = [113.8575, 122.961, 125.307]
  # CIFAR_100_MEAN = [0.5071, 0.4867, 0.4408]
  # CIFAR_100_STD = [0.2675, 0.2565, 0.2761]
  # CIFAR_100_BGR_255 = [129.3105, 124.1085, 112.404]
  # IMAGENET_MEAN = [0.485, 0.456, 0.406]
  # IMAGENET_STD = [0.229, 0.224, 0.225]
  # IMAGENET_MEAN_BGR_255 = [103.939, 116.779, 123.68]

[ Params ]:

# ArchitectureParams(task=<Task.CLASSIFICATION: 'classification'>, num_classes=10, ensemble_multiplier=1, custom_objects={}, model_config=None, 
# backbone_only=False, input_shape=(32, 32, 3), input_range=<ImageRange.CIFAR100_TORCH: 'CIFAR100_TORCH'>, input_dtype=tf.float32, input_channels_last=True, 
# prediction_logits=True, prediction_dtype=tf.float32, prediction_structure=<TensorStructure.SINGLE_TENSOR: 'single_tensor'>, prediction_shape=TensorShape([10]))

Solution 2:^[2]

This Value Error is explaining the problem its the message.

The dataset labels, being CIFAR-100, are one hot vectors of length 100.

But the call to "masterful.data.learn_data_params" gets passed the value 10.

Update your model architecture with:

  model.add(tf.keras.layers.Dense(100))

and your call to learn_data_params with:

    num_classes=100,

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution	Source
Solution 1	Martijn Pieters
Solution 2	Yaoshiang

'Getting ValueError: Data Params Error when using masterful.trainer.train

Solution 1:[1]

Solution 2:[2]

Sources

Related Questions

Solution 1:^[1]

Solution 2:^[2]